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Utilizing decision tree machine model to map dental students' preferred learning styles with suitable instructional strategies

BackgroundGrowing demand for student-centered learning (SCL) has been observed in higher education settings including dentistry. However, application of SCL in dental education is limited. Hence, this study aimed to facilitate SCL application in dentistry utilising a decision tree machine learning (...

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Main Authors: Shoaib, Lily Azura, Safii, Syarida Hasnur, Idris, Norisma, Hussin, Ruhaya, Sazali, Muhamad Amin Hakim
Format: Article
Published: BMC 2024
Subjects:
LB2300 Higher Education
RK Dentistry
Online Access:http://eprints.um.edu.my/44172/
https://doi.org/10.1186/s12909-023-05022-5
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spelling my.um.eprints.441722024-06-14T05:46:11Z http://eprints.um.edu.my/44172/ Utilizing decision tree machine model to map dental students' preferred learning styles with suitable instructional strategies Shoaib, Lily Azura Safii, Syarida Hasnur Idris, Norisma Hussin, Ruhaya Sazali, Muhamad Amin Hakim LB2300 Higher Education RK Dentistry BackgroundGrowing demand for student-centered learning (SCL) has been observed in higher education settings including dentistry. However, application of SCL in dental education is limited. Hence, this study aimed to facilitate SCL application in dentistry utilising a decision tree machine learning (ML) technique to map dental students' preferred learning styles (LS) with suitable instructional strategies (IS) as a promising approach to develop an IS recommender tool for dental students.MethodsA total of 255 dental students in Universiti Malaya completed the modified Index of Learning Styles (m-ILS) questionnaire containing 44 items which classified them into their respective LS. The collected data, referred to as dataset, was used in a decision tree supervised learning to automate the mapping of students' learning styles with the most suitable IS. The accuracy of the ML-empowered IS recommender tool was then evaluated.ResultsThe application of a decision tree model in the automation process of the mapping between LS (input) and IS (target output) was able to instantly generate the list of suitable instructional strategies for each dental student. The IS recommender tool demonstrated perfect precision and recall for overall model accuracy, suggesting a good sensitivity and specificity in mapping LS with IS.ConclusionThe decision tree ML empowered IS recommender tool was proven to be accurate at matching dental students' learning styles with the relevant instructional strategies. This tool provides a workable path to planning student-centered lessons or modules that potentially will enhance the learning experience of the students. BMC 2024-01-11 Article PeerReviewed Shoaib, Lily Azura and Safii, Syarida Hasnur and Idris, Norisma and Hussin, Ruhaya and Sazali, Muhamad Amin Hakim (2024) Utilizing decision tree machine model to map dental students' preferred learning styles with suitable instructional strategies. BMC Medical Education, 24 (1). ISSN 1472-6920, DOI https://doi.org/10.1186/s12909-023-05022-5 <https://doi.org/10.1186/s12909-023-05022-5>. https://doi.org/10.1186/s12909-023-05022-5 10.1186/s12909-023-05022-5
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic LB2300 Higher Education
RK Dentistry
spellingShingle LB2300 Higher Education
RK Dentistry
Shoaib, Lily Azura
Safii, Syarida Hasnur
Idris, Norisma
Hussin, Ruhaya
Sazali, Muhamad Amin Hakim
Utilizing decision tree machine model to map dental students' preferred learning styles with suitable instructional strategies
description BackgroundGrowing demand for student-centered learning (SCL) has been observed in higher education settings including dentistry. However, application of SCL in dental education is limited. Hence, this study aimed to facilitate SCL application in dentistry utilising a decision tree machine learning (ML) technique to map dental students' preferred learning styles (LS) with suitable instructional strategies (IS) as a promising approach to develop an IS recommender tool for dental students.MethodsA total of 255 dental students in Universiti Malaya completed the modified Index of Learning Styles (m-ILS) questionnaire containing 44 items which classified them into their respective LS. The collected data, referred to as dataset, was used in a decision tree supervised learning to automate the mapping of students' learning styles with the most suitable IS. The accuracy of the ML-empowered IS recommender tool was then evaluated.ResultsThe application of a decision tree model in the automation process of the mapping between LS (input) and IS (target output) was able to instantly generate the list of suitable instructional strategies for each dental student. The IS recommender tool demonstrated perfect precision and recall for overall model accuracy, suggesting a good sensitivity and specificity in mapping LS with IS.ConclusionThe decision tree ML empowered IS recommender tool was proven to be accurate at matching dental students' learning styles with the relevant instructional strategies. This tool provides a workable path to planning student-centered lessons or modules that potentially will enhance the learning experience of the students.
format Article
author Shoaib, Lily Azura
Safii, Syarida Hasnur
Idris, Norisma
Hussin, Ruhaya
Sazali, Muhamad Amin Hakim
author_facet Shoaib, Lily Azura
Safii, Syarida Hasnur
Idris, Norisma
Hussin, Ruhaya
Sazali, Muhamad Amin Hakim
author_sort Shoaib, Lily Azura
title Utilizing decision tree machine model to map dental students' preferred learning styles with suitable instructional strategies
title_short Utilizing decision tree machine model to map dental students' preferred learning styles with suitable instructional strategies
title_full Utilizing decision tree machine model to map dental students' preferred learning styles with suitable instructional strategies
title_fullStr Utilizing decision tree machine model to map dental students' preferred learning styles with suitable instructional strategies
title_full_unstemmed Utilizing decision tree machine model to map dental students' preferred learning styles with suitable instructional strategies
title_sort utilizing decision tree machine model to map dental students' preferred learning styles with suitable instructional strategies
publisher BMC
publishDate 2024
url http://eprints.um.edu.my/44172/
https://doi.org/10.1186/s12909-023-05022-5
_version_ 1805881129830449152
score 13.211869

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